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» Learning Models for Object Recognition
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143
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ICPR
2006
IEEE
16 years 4 months ago
Combining Generative and Discriminative Methods for Pixel Classification with Multi-Conditional Learning
It is possible to broadly characterize two approaches to probabilistic modeling in terms of generative and discriminative methods. Provided with sufficient training data the discr...
B. Michael Kelm, Chris Pal, Andrew McCallum
JMLR
2012
13 years 5 months ago
Bounding the Probability of Error for High Precision Optical Character Recognition
We consider a model for which it is important, early in processing, to estimate some variables with high precision, but perhaps at relatively low recall. If some variables can be ...
Gary B. Huang, Andrew Kae, Carl Doersch, Erik G. L...
CVPR
2007
IEEE
16 years 5 months ago
Automatic Face Recognition from Skeletal Remains
The ability to determine the identity of a skull found at a crime scene is of critical importance to the law enforcement community. Traditional clay-based methods attempt to recon...
Carl Adrian, Nils Krahnstoever, Peter H. Tu, Phil ...
130
Voted
CVPR
2009
IEEE
16 years 10 months ago
Learning to Track with Multiple Observers
We propose a novel approach to designing algorithms for object tracking based on fusing multiple observation models. As the space of possible observation models is too large for...
Björn Stenger, Roberto Cipolla, Thomas Woodle...
NIPS
2008
15 years 4 months ago
Dimensionality Reduction for Data in Multiple Feature Representations
In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. These representa...
Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh